
"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Union[str, Any]       		=		logging.get_logger(__name__)


def 			_UpperCamelCase						(    _A   )     ->	Any:





    """simple docstring"""

    if "resnet-50" in model_name:
        _UpperCAmelCase        = ResNetConfig.from_pretrained("""microsoft/resnet-50"""   )
    elif "resnet-101" in model_name:
        _UpperCAmelCase        = ResNetConfig.from_pretrained("""microsoft/resnet-101"""   )
    else:
        raise ValueError("""Model name should include either resnet50 or resnet101"""   )

    _UpperCAmelCase        = DetrConfig(use_timm_backbone=_A       ,   backbone_config=_A   )

    # set label attributes
    _UpperCAmelCase        = """panoptic""" in model_name
    if is_panoptic:
        _UpperCAmelCase        = 2_5_0
    else:
        _UpperCAmelCase        = 9_1
        _UpperCAmelCase        = """huggingface/label-files"""
        _UpperCAmelCase        = """coco-detection-id2label.json"""
        _UpperCAmelCase        = json.load(open(hf_hub_download(_A       ,   _A       ,   repo_type="""dataset"""   )       ,   """r"""   )   )
        _UpperCAmelCase        = {int(_A   ): v for k, v in idalabel.items()}
        _UpperCAmelCase        = idalabel
        _UpperCAmelCase        = {v: k for k, v in idalabel.items()}

    return config, is_panoptic


def 			_UpperCamelCase						(    _A   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = []

    # stem
    # fmt: off
    rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")   )
    rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")   )
    rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")   )
    rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")   )
    rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")   )
    # stages
    for stage_idx in range(len(config.backbone_config.depths   )   ):
        for layer_idx in range(config.backbone_config.depths[stage_idx]   ):
            # shortcut
            if layer_idx == 0:
                rename_keys.append(
                    (
                        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                    )   )
                rename_keys.append(
                    (
                        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                    )   )
                rename_keys.append(
                    (
                        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                    )   )
                rename_keys.append(
                    (
                        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                    )   )
                rename_keys.append(
                    (
                        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                    )   )
            # 3 convs
            for i in range(3   ):
                rename_keys.append(
                    (
                        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                    )   )
                rename_keys.append(
                    (
                        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                    )   )
                rename_keys.append(
                    (
                        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                    )   )
                rename_keys.append(
                    (
                        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                    )   )
                rename_keys.append(
                    (
                        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                    )   )
    # fmt: on

    for i in range(config.encoder_layers   ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append(
            (
                F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
                F"""encoder.layers.{i}.self_attn.out_proj.weight""",
            )   )
        rename_keys.append(
            (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")   )
        rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight""")   )
        rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias""")   )
        rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight""")   )
        rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias""")   )
        rename_keys.append(
            (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")   )
        rename_keys.append(
            (F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias""")   )
        rename_keys.append(
            (F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight""")   )
        rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias""")   )
        # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
        rename_keys.append(
            (
                F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
                F"""decoder.layers.{i}.self_attn.out_proj.weight""",
            )   )
        rename_keys.append(
            (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")   )
        rename_keys.append(
            (
                F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
                F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
            )   )
        rename_keys.append(
            (
                F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
                F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
            )   )
        rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight""")   )
        rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias""")   )
        rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight""")   )
        rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias""")   )
        rename_keys.append(
            (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")   )
        rename_keys.append(
            (F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias""")   )
        rename_keys.append(
            (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")   )
        rename_keys.append(
            (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")   )
        rename_keys.append(
            (F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight""")   )
        rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias""")   )

    # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
    rename_keys.extend(
        [
            ("""input_proj.weight""", """input_projection.weight"""),
            ("""input_proj.bias""", """input_projection.bias"""),
            ("""query_embed.weight""", """query_position_embeddings.weight"""),
            ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
            ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
            ("""class_embed.weight""", """class_labels_classifier.weight"""),
            ("""class_embed.bias""", """class_labels_classifier.bias"""),
            ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
            ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
            ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
            ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
            ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
            ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
        ]   )

    return rename_keys


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = state_dict.pop(_A   )
    _UpperCAmelCase        = val


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = """"""
    if is_panoptic:
        _UpperCAmelCase        = """detr."""

    # first: transformer encoder
    for i in range(6   ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase        = in_proj_weight[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias[:2_5_6]
        _UpperCAmelCase        = in_proj_weight[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias[-2_5_6:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6   ):
        # read in weights + bias of input projection layer of self-attention
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase        = in_proj_weight[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias[:2_5_6]
        _UpperCAmelCase        = in_proj_weight[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias[-2_5_6:]
        # read in weights + bias of input projection layer of cross-attention
        _UpperCAmelCase        = state_dict.pop(
            F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        _UpperCAmelCase        = in_proj_weight_cross_attn[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[:2_5_6]
        _UpperCAmelCase        = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight_cross_attn[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[-2_5_6:]


def 			_UpperCamelCase						(    )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase        = """http://images.cocodataset.org/val2017/000000039769.jpg"""
    _UpperCAmelCase        = Image.open(requests.get(_A       ,   stream=_A   ).raw   )

    return im


@torch.no_grad()
def 			_UpperCamelCase						(    _A       ,   _A=None       ,   _A=False   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = get_detr_config(_A   )

    # load original model from torch hub
    _UpperCAmelCase        = {
        """detr-resnet-50""": """detr_resnet50""",
        """detr-resnet-101""": """detr_resnet101""",
    }
    logger.info(F"""Converting model {model_name}..."""   )
    _UpperCAmelCase        = torch.hub.load("""facebookresearch/detr"""       ,   model_name_to_original_name[model_name]       ,   pretrained=_A   ).eval()
    _UpperCAmelCase        = detr.state_dict()
    # rename keys
    for src, dest in create_rename_keys(_A   ):
        if is_panoptic:
            _UpperCAmelCase        = """detr.""" + src
        rename_key(_A       ,   _A       ,   _A   )
    # query, key and value matrices need special treatment
    read_in_q_k_v(_A       ,   is_panoptic=_A   )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    _UpperCAmelCase        = """detr.model.""" if is_panoptic else """model."""
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith("""detr"""   )
                and not key.startswith("""class_labels_classifier"""   )
                and not key.startswith("""bbox_predictor"""   )
            ):
                _UpperCAmelCase        = state_dict.pop(_A   )
                _UpperCAmelCase        = val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                _UpperCAmelCase        = state_dict.pop(_A   )
                _UpperCAmelCase        = val
            elif key.startswith("""bbox_attention"""   ) or key.startswith("""mask_head"""   ):
                continue
            else:
                _UpperCAmelCase        = state_dict.pop(_A   )
                _UpperCAmelCase        = val
        else:
            if not key.startswith("""class_labels_classifier"""   ) and not key.startswith("""bbox_predictor"""   ):
                _UpperCAmelCase        = state_dict.pop(_A   )
                _UpperCAmelCase        = val

    # finally, create HuggingFace model and load state dict
    _UpperCAmelCase        = DetrForSegmentation(_A   ) if is_panoptic else DetrForObjectDetection(_A   )
    model.load_state_dict(_A   )
    model.eval()

    # verify our conversion on an image
    _UpperCAmelCase        = """coco_panoptic""" if is_panoptic else """coco_detection"""
    _UpperCAmelCase        = DetrImageProcessor(format=_A   )

    _UpperCAmelCase        = processor(images=prepare_img()       ,   return_tensors="""pt"""   )
    _UpperCAmelCase        = encoding["""pixel_values"""]

    _UpperCAmelCase        = detr(_A   )
    _UpperCAmelCase        = model(_A   )

    assert torch.allclose(outputs.logits       ,   original_outputs["""pred_logits"""]       ,   atol=1e-3   )
    assert torch.allclose(outputs.pred_boxes       ,   original_outputs["""pred_boxes"""]       ,   atol=1e-3   )
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks       ,   original_outputs["""pred_masks"""]       ,   atol=1e-4   )
    print("""Looks ok!"""   )

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""   )
        Path(_A   ).mkdir(exist_ok=_A   )
        model.save_pretrained(_A   )
        processor.save_pretrained(_A   )

    if push_to_hub:
        # Upload model and image processor to the hub
        logger.info("""Uploading PyTorch model and image processor to the hub..."""   )
        model.push_to_hub(F"""nielsr/{model_name}"""   )
        processor.push_to_hub(F"""nielsr/{model_name}"""   )


if __name__ == "__main__":
 a       :						Tuple       		=		argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name''',
     default='''detr-resnet-50''',
     type=str,
     choices=['''detr-resnet-50''', '''detr-resnet-101'''],
     help='''Name of the DETR model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
 a       :						str       		=		parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""



import warnings
from functools import wraps
from typing import Callable


def 			_UpperCamelCase						(    _A   )     ->	Callable:





    """simple docstring"""

    @wraps(_A   )
    def _inner_fn(*_A       ,   **_A   ):
        warnings.warn(
            (F"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")       ,   _A       ,   )
        return fn(*_A       ,   **_A   )

    return _inner_fn